Left ventricular segmentation from MRI datasets with edge modelling conditional random fields.

ABSTRACT:

Background

This paper considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. Various aspects, such as the presence of papillary muscles near the endocardium border, makes simple threshold based segmentation difficult.

Methods

The endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion. Image features are derived from edge information from human annotated images. The features are combined within a discriminatively trained Conditional Random Field (CRF). Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell's method is applied to find CRF parameters by minimizing the difference between ground truth annotation

SUBMITTER: Dreijer JF 

PROVIDER: S-EPMC3737053 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature